CN113888869B

CN113888869B - Fake plate slag car networking positioning method and system and cloud platform

Info

Publication number: CN113888869B
Application number: CN202111194358.9A
Authority: CN
Inventors: 杨翰翔; 付正武
Original assignee: Shenzhen Lianhe Intelligent Technology Co ltd
Current assignee: Shenzhen Lianhe Intelligent Technology Co ltd
Priority date: 2021-10-13
Filing date: 2021-10-13
Publication date: 2022-08-09
Anticipated expiration: 2041-10-13
Also published as: CN113888869A

Abstract

The embodiment of the invention provides a fake plate muck vehicle networking positioning method, a system and a cloud platform, wherein the method comprises the steps of obtaining a muck vehicle moving route of a target muck vehicle and a vehicle license moving route of a muck vehicle certificate in a preset time period, carrying out matching analysis on the muck vehicle moving route and the vehicle license moving route, determining an abnormal vehicle certificate moving route existing in the vehicle license moving route, determining a muck vehicle certificate corresponding to the abnormal vehicle certificate moving route as a target monitoring muck vehicle certificate, obtaining a significant characteristic identifier of a current muck vehicle where the target monitoring muck vehicle certificate is located when the target monitoring muck vehicle certificate is monitored subsequently, inquiring a vehicle positioning module corresponding to the target muck vehicle matched with the significant characteristic identifier from a database according to the significant characteristic identifier, obtaining first positioning information of the target muck vehicle through the vehicle positioning module, obtaining second positioning information of the target monitoring muck vehicle certificate, and carrying out positioning monitoring on the target monitoring muck vehicle certificate and the target muck vehicle if the positioning is consistent.

Description

Fake plate slag car networking positioning method and system and cloud platform

Technical Field

The invention relates to the technical field of car networking and muck car monitoring, in particular to a fake plate muck car networking positioning method and system and a cloud platform.

Background

The muck truck is also called a soil pulling truck and a muck truck, and is a large-scale load-carrying truck such as a common large-scale dump truck and a truck for conveying muck materials such as sand and stone. The muck truck is large in size and high in cab, and has many vision blind areas, particularly in the areas right in front of and behind the truck and in the front wheel on the right side of the truck. In addition, the problems of frequent traffic violation, frequent collision, urban environment pollution, influence on the life of residents and the like of the residue soil vehicle become targets of public opinion discussion. Based on this, documentation on the transformation of muck trucks has also begun to be carried out nationwide.

In some specific muck vehicle application scenes, in order to realize effective monitoring of muck vehicles, vehicle certificates (such as vehicle cards based on RFID) of different muck vehicles are bound with different muck vehicles (such as license plate numbers), and management and monitoring of the vehicles can be realized through integration of the vehicle certificates subsequently. However, due to benefit driving, the muck vehicle fake-licensed behavior is often prohibited in practical scenes, and further causes a problem of difficult supervision for related supervision departments. For example, the license plate is sleeved on other similar-shape muck cars, so that the monitoring and management of the combination of the car and the certificate can be avoided. Most of the existing supervision of the dump trucks depends on manual random spot check supervision, large-scale supervision is difficult to realize, the efficiency is low, and the effect is poor.

In addition, in order to realize real-time positioning monitoring of the muck truck, the installation of a positioning module (such as a GPS positioning module) on the muck truck becomes a trend of the existing muck truck management. Based on the above, how to realize the positioning monitoring and identification of the fake-licensed muck truck by combining the management of the positioning module and the vehicle-license integration is a great direction of research in the field.

Disclosure of Invention

In view of the above mentioned problems, an embodiment of the present invention provides a fake-licensed dregs car networking positioning method, which is applied to a cloud platform, and the method includes:

acquiring a muck vehicle moving route of a target muck vehicle and a vehicle license moving route of a muck vehicle license corresponding to the target muck vehicle within a preset time period;

matching and analyzing the movement route of the muck vehicle and the movement route of the car license to determine whether an abnormal car license movement route exists in the movement route of the car license;

when the fact that the abnormal vehicle license moving route exists in the vehicle license moving route is determined, determining a muck vehicle license corresponding to the abnormal vehicle license moving route as a target monitoring muck vehicle license;

when the target monitoring muck vehicle certificate is monitored, acquiring a significant feature identifier of a current muck vehicle where the target monitoring muck vehicle certificate is located, and inquiring a vehicle positioning module corresponding to the target muck vehicle matched with the significant feature identifier from a database according to the significant feature identifier;

and acquiring first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and judging whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information.

In one possible real-time approach, the method further comprises:

and when the target monitoring muck vehicle certificate is judged to be inconsistent with the target muck vehicle positioning according to the first positioning information and the second positioning information, determining that the target monitoring muck vehicle certificate has abnormal use behaviors, and sending alarm information to set monitoring terminal equipment so as to inform related personnel to process the abnormal use behaviors.

In a possible real-time manner, the performing matching analysis on the muck vehicle movement route and the vehicle license movement route to determine whether an abnormal vehicle license movement route exists in the vehicle license movement route includes:

matching the movement route of the muck vehicle and the movement route of the vehicle license in the preset time period based on the movement route matching relationship between the movement route of the muck vehicle and the movement route of the vehicle license in the preset time period to obtain a plurality of movement route combinations;

determining the matched remaining vehicle license moving route as a vehicle license moving route to be processed, and acquiring first moving route description information of the vehicle license moving route to be processed according to first vehicle license position information contained in the vehicle license moving route to be processed; the first license location information is generated in the preset time period; the vehicle license moving route in each moving route combination respectively comprises second vehicle license position information in the preset time period;

respectively acquiring second moving route description information of the vehicle license moving route in each moving route combination according to second vehicle license position information included in each moving route combination;

acquiring characteristic differences between the first moving route description information and second moving route description information corresponding to the vehicle license moving route in each moving route combination;

according to the characteristic difference corresponding to each mobile route combination, determining the route association degree between the vehicle license mobile route in each mobile route combination and the vehicle license mobile route to be processed;

when the number of abnormal evidence moving routes with the corresponding route relevance smaller than the preset relevance reaches the set number of routes, taking the muck evidence corresponding to the abnormal evidence moving routes as a target monitoring muck evidence; the abnormal vehicle license moving route is included in the vehicle license moving route to be processed.

In a possible real-time mode, the number of the first license position information is multiple; the obtaining of the first movement route description information of the movement route of the vehicle license to be processed according to the first vehicle license position information included in the movement route of the vehicle license to be processed includes:

acquiring position description information corresponding to each piece of first license position information in the plurality of pieces of first license position information;

acquiring a first position information sequence corresponding to the plurality of first vehicle license position information according to the position description information corresponding to each first vehicle license position information;

determining the first position information sequence as the first moving route description information;

the obtaining of the second moving route description information of the vehicle license moving route in each moving route combination according to the second vehicle license location information included in each moving route combination includes:

for each moving route combination, acquiring position description information corresponding to each piece of second vehicle license position information in a plurality of pieces of second vehicle license position information included in the moving route combination;

acquiring a second position information sequence corresponding to the plurality of second vehicle license position information according to the position description information corresponding to each second vehicle license position information;

and determining the second position information sequence as second moving route description information of the vehicle license moving route in the moving route combination.

In a possible real-time manner, the obtaining a muck vehicle moving route of a target muck vehicle and a vehicle license moving route of a muck vehicle license corresponding to the target muck vehicle within a preset time period includes:

acquiring position information of a plurality of muck cars and position information of a plurality of car certificates in the preset time period;

acquiring the muck vehicle position information correlation degree and the muck vehicle position distance among the plurality of muck vehicle position information, and acquiring the vehicle license position information correlation degree and the vehicle license position distance among the plurality of vehicle license position information;

combining the position information of the plurality of muck vehicles according to the relevance of the position information of the muck vehicles and the position distance of the muck vehicles to obtain a moving route of the muck vehicles in the preset time period; wherein, one moving route of the slag car comprises at least two pieces of slag car position information;

combining the plurality of pieces of vehicle license position information according to the vehicle license position information association degree and the vehicle license position distance to obtain a vehicle license moving route in the preset time period; one vehicle license moving route comprises at least two pieces of vehicle license position information;

the matching of the muck vehicle moving route and the vehicle license moving route in the preset time period is carried out based on the moving route matching relationship between the muck vehicle moving route and the vehicle license moving route in the preset time period to obtain a plurality of moving route combinations, and the method comprises the following steps:

determining the vehicle license moving route in the preset time period as a vehicle license moving route to be matched, and determining the muck vehicle moving route in the preset time period as a muck vehicle moving route to be matched; the vehicle license position information in the vehicle license moving route to be matched is acquired through the target vehicle license identification equipment within the preset time period;

acquiring the position information of the muck car in the target car license identification equipment;

determining the position information correlation degree between the position information of the muck vehicle in the target vehicle license identification equipment and the position information of the muck vehicle in the moving route of the muck vehicle to be matched as the moving route matching relation between the moving route of the muck vehicle to be matched and the moving route of the muck vehicle to be matched;

and when the matching relation of the moving routes reaches a set condition, matching the moving route of the license to be matched with the moving route of the muck vehicle to be matched to obtain a plurality of moving route combinations.

In one possible real-time approach, the method further comprises:

when the target monitoring muck vehicle certificate is judged to be inconsistent with the target muck vehicle positioning according to the first positioning information and the second positioning information, acquiring a monitoring video of a current muck vehicle where the target monitoring muck vehicle certificate is located;

carrying out fake plate identification on the current slag car according to the monitoring video, and judging whether fake plate behaviors exist in the current slag car or not;

and when judging that the current muck truck has the fake plate behavior, sending alarm information to set monitoring terminal equipment to inform related personnel to process the fake plate behavior.

In a possible real-time manner, the performing fake plate recognition on the current slag car according to the monitoring video and judging whether the current slag car has fake plate behavior includes:

sequentially acquiring a monitoring image frame from the monitoring video as a current monitoring image frame and acquiring at least one monitoring image frame behind the current monitoring image frame as a fusion monitoring image frame;

respectively acquiring key information of the current monitoring image frame and the fused monitoring image frame according to the significance identification information of the current muck truck to obtain a first key visual feature of the current monitoring image frame and a second key visual feature of the fused monitoring image frame, wherein the first key visual feature is at least used for representing the feature association degree between the visual feature included in the current monitoring image frame and the significance identification information, and the second key visual feature is at least used for representing the feature association degree between the visual feature included in the fused monitoring image frame and the significance identification information;

combining the first key visualization feature and the second key visualization feature to obtain a key visualization combination feature of the current monitoring image frame;

performing matching degree analysis on the key visual combined feature and a standard visual feature which is stored in the database and corresponds to the significance identification information, and judging that no fake plate behavior exists in the current muck truck when the standard visual feature which is stored in the database and corresponds to the significance identification information is matched with the key visual combined feature;

and when the standard visual characteristics corresponding to the significance identification information and the key visual combination characteristics stored in the database are not matched, judging that the current muck truck has a fake plate behavior.

In a possible real-time manner, acquiring key information of the current monitoring image frame according to the saliency identification information to obtain a first key visualization feature of the current monitoring image frame, including:

acquiring key information of the significance identification information to obtain an identification feature vector of the significance identification information;

acquiring key information of two or more interested areas in the current monitoring image frame to obtain the key information of the interested areas of the two or more interested areas;

determining a region association level of the two or more regions of interest based on the identification feature vector and region of interest key information of the two or more regions of interest, wherein the region association level of the regions of interest characterizes a feature association degree between the regions of interest and the significance identification information;

performing information fusion on the key information of the interest areas of the two or more interest areas based on the area association levels of the two or more interest areas to obtain the first key visualization feature;

when the fused monitoring image frame comprises two or more than two, the combining the first key visualization feature and the second key visualization feature to obtain the key visualization combination feature of the current monitoring image frame includes:

acquiring a fused monitoring image frame corresponding to a maximum second correlation parameter from the two or more fused monitoring image frames according to a second correlation parameter between the first key visualization feature and two or more second key visualization features, wherein the second correlation parameter is at least used for representing a feature difference degree between the visualization feature included in the fused monitoring image frame and the visualization feature included in the current monitoring image frame;

and combining the first key visualization feature and the acquired second key visualization feature of the fusion monitoring image frame to obtain the key visualization combined feature.

On the other hand, the embodiment of the invention also provides a fake plate muck car networking positioning system, which comprises

The system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a muck vehicle moving route of a target muck vehicle and a vehicle license moving route of a muck vehicle license corresponding to the target muck vehicle within a preset time period;

the matching analysis module is used for performing matching analysis on the muck vehicle moving route and the vehicle license moving route to determine whether an abnormal vehicle license moving route exists in the vehicle license moving route;

the abnormity determining module is used for determining the muck vehicle certificate corresponding to the abnormal vehicle certificate moving route as a target monitoring muck vehicle certificate when the abnormal vehicle certificate moving route is determined to exist in the vehicle certificate moving routes;

the second acquisition module is used for acquiring the significant characteristic identifier of the current muck truck where the target monitoring muck truck certificate is located when the target monitoring muck truck certificate is monitored, and inquiring a vehicle positioning module corresponding to the target muck truck matched with the significant characteristic identifier from a database according to the significant characteristic identifier; and

and the positioning analysis module is used for acquiring first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and judging whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information.

In still another aspect, an embodiment of the present invention further provides a cloud platform, which includes a processor, and a machine-readable storage medium, where the machine-readable storage medium is connected to the processor, and the machine-readable storage medium is used to store a program, an instruction, or code, and the processor is used to execute the program, the instruction, or the code in the machine-readable storage medium, so as to implement the foregoing method.

In summary, according to the fake-licensed muck car networking positioning method, system and cloud platform provided by the embodiments of the present invention, a muck car moving route of a target muck car and a car license moving route of a muck car license corresponding to the target muck car in a preset time period are obtained, so as to perform matching analysis on the muck car moving route and the car license moving route, and determine whether an abnormal car license moving route exists in the car license moving route. When the abnormal vehicle license moving route is determined to exist in the vehicle license moving route, determining the muck vehicle license corresponding to the abnormal vehicle license moving route as a target monitoring muck vehicle license, acquiring the significance characteristic identification of the current muck vehicle where the target monitoring muck vehicle license is located when the target monitoring muck vehicle license is monitored subsequently, and inquiring a vehicle positioning module corresponding to the target muck vehicle matched with the significance characteristic identification from a database according to the significance characteristic identification. And finally, acquiring first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and judging whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information. And when the target monitoring muck vehicle certificate is inconsistent with the target muck vehicle in positioning, determining that the target monitoring muck vehicle certificate has abnormal use behaviors, and sending alarm information to set monitoring terminal equipment so as to inform related personnel to process the abnormal use behaviors. Therefore, the abnormal condition identification of the muck car license can be realized through the combination of the moving routes of the muck car license and the muck car, and the muck car corresponding to the abnormal condition is subjected to networking positioning monitoring.

In addition, automatic identification of the fake-licensed muck car is further realized by combining with a monitoring video of a target monitoring muck car certificate site. Meanwhile, the identification accuracy of the fake-licensed muck truck can be improved by a method of performing fusion analysis on the current monitoring image frame and the subsequent fusion monitoring image frame in the sequentially acquired monitoring video.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic flow chart of a fake-licensed muck car networking positioning method provided by an embodiment of the invention.

Fig. 2 is a schematic diagram of an application scenario architecture for implementing the fake-licensed muck car networking positioning method.

Fig. 3 is a schematic diagram of the cloud platform of fig. 2.

Fig. 4 is a flow chart illustrating the sub-steps of step S200 in fig. 1.

Fig. 5 is a schematic functional module diagram of a fake-licensed muck car networking positioning system provided in an embodiment of the present invention.

Detailed Description

Please refer to fig. 1, which is a flowchart illustrating a method for positioning a fake-licensed dregs car networking according to an embodiment of the present invention. Firstly, a hardware environment for realizing the fake-licensed muck car networking positioning method is explained.

As shown in fig. 2, the method may be performed and implemented by a cloud platform 20 for remote and monitoring of a muck truck. In this embodiment, the cloud platform 20 may be a service platform that is pre-established for communication with devices (such as the vehicle license identification device 110 and the vehicle monitoring device 120) for performing relevant monitoring and identification on the muck truck 10. The vehicle license identification device 110 may be, for example, but not limited to, an FRID reading device that reads and identifies information of an RFID vehicle card certificate. The vehicle monitoring device 120 may be, for example, but not limited to, a vehicle management and monitoring system installed in a site related to the muck truck 10, such as a vehicle management and monitoring system for license plate recognition and video monitoring of the muck truck. By way of example, the cloud platform 20 may be, but is not limited to, a computer device, a server, a computer device, a cloud service center, a computer room control center, a cloud platform, and the like, which have communication control capability and big data analysis capability. Preferably, in this embodiment, the cloud platform 20 is taken as an example of a server, and the server may be an independent server, or may be a server cluster, a cloud server, a remote server center, and the like, which are formed by two or more servers.

Further, as shown with reference to fig. 3, the cloud platform 20 may include a machine-readable medium 21, a processor 22, a communication bus 23, and a fake-licensed dregs car networking positioning system 24. In this embodiment, the machine-readable medium 21, the processor 22, and the communication bus 23 may be directly or indirectly electrically connected to each other to enable transmission or interaction of data. These components may be electrically connected to each other via one or more of the communication buses 23, for example. The machine-readable medium 21 may be any type of storage unit, for example, the present embodiment is preferably a non-volatile machine-readable storage medium. The machine-readable medium 21 stores therein various types of programs, instructions or executable code, such as software program portions corresponding to various software functional modules included in the fake-licensed dregs car networking positioning system 24. The fake-licensed dregs car networking positioning system 24 may include at least one software functional module stored in the machine readable medium 21 in the form of software or firmware (firmware), and the processor 22 implements various functional applications and data processing of the cloud platform 20, for example, implements the business image analysis method in the embodiment of the present application, by running the software programs and modules stored in the machine readable medium 21, for example, the software programs and modules in the fake-licensed dregs car networking positioning system 24 in the embodiment of the present application.

The machine-readable medium 21 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The processor 22 may be an integrated circuit chip having data processing capabilities. The Processor 22 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like, for implementing or executing the methods, steps, and logic blocks disclosed in the embodiments of the present application.

Further, the communication bus 23 may be used to implement communication connection between the components of the cloud platform 20, and also implement communication connection between the communication components inside the cloud platform 20 and external communication devices, thereby implementing transmission of network signals and data.

The following describes in detail, by way of example, the various steps involved in the fake-licensed dregs car networking location method shown in fig. 1. Optionally, the method may include the steps described in S100-S500 below, which are specifically described below.

Step S100, a muck vehicle moving route of a target muck vehicle and a vehicle license moving route of a muck vehicle license corresponding to the target muck vehicle in a preset time period are obtained.

Specifically, in this embodiment, the target slag car may be any one or more slag cars in a monitored scene or a monitored slag car group. The movement route of the muck truck can be obtained according to the relevant information (such as position information, task item information and the like) of the target muck truck fed back by the history of the vehicle monitoring equipment, and the movement route of the car license can be obtained through the history monitoring data fed back by identifying the muck truck license by the car license identifying equipment, or can be obtained from the history positioning information recorded by the muck truck license when the muck truck license has the information storage function. The preset time period may be a time period set according to a preset monitoring cycle.

And step S200, performing matching analysis on the movement route of the muck vehicle and the movement route of the car license, and determining whether an abnormal car license movement route exists in the movement route of the car license.

In this embodiment, the muck vehicle movement route and the vehicle license movement route are subjected to matching analysis to determine whether an abnormal vehicle license movement route exists in the vehicle license movement route, and whether a vehicle license movement route which does not match with the muck vehicle movement route exists is mainly judged according to a route difference between the muck vehicle movement route and the vehicle license movement route as the abnormal vehicle license movement route, so that the method can be used for analyzing whether the muck vehicle license is illegally borrowed or stolen, and the like, and a specific matching analysis method will be described in detail later.

Step S300, when the fact that the abnormal vehicle license moving route exists in the vehicle license moving route is determined, determining the muck vehicle license corresponding to the abnormal vehicle license moving route as a target monitoring muck vehicle license.

In this embodiment, the muck vehicle license corresponding to the abnormal vehicle license moving route is determined as the target monitoring muck vehicle license, and the method can be used for performing key monitoring and identification on the target monitoring muck vehicle license in the following so as to avoid the later-stage muck vehicle violation phenomenon and the like, and the specific method will be described in detail later.

And S400, when the target monitoring muck vehicle certificate is monitored, acquiring a significant feature identifier of the current muck vehicle where the target monitoring muck vehicle certificate is located, and inquiring a vehicle positioning module corresponding to the target muck vehicle matched with the significant feature identifier from a database according to the significant feature identifier.

In this embodiment, the significant feature identifier may be a license plate number, a license plate number + a vehicle logo, and the like, which correspond to the muck vehicle at present, and is not limited specifically. Based on the fact that the corresponding vehicle positioning module can be inquired from a pre-stored database in a cloud platform according to the significance identification such as the license plate number, the corresponding vehicle positioning module is used for obtaining whether the current real-time position of the target muck vehicle (real legal muck vehicle) uniquely associated with the significance characteristic identification is consistent with the current position of the target monitoring muck vehicle certificate or not according to the vehicle positioning module subsequently, and further judging whether phenomena such as illegal use of the muck vehicle certificate exist or not.

Step S500, acquiring first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and judging whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information.

In this embodiment, if the first positioning information and the second positioning information are different or not matched (the difference of the matching degrees exceeds the threshold), it may indicate that the target monitoring muck vehicle certificate is inconsistent with the target muck vehicle positioning, and an abnormal phenomenon may occur, and it is necessary to notify related devices and personnel to perform verification processing.

For example, when it is determined that the target monitoring muck vehicle identification is inconsistent with the target muck vehicle positioning according to the first positioning information and the second positioning information, it may be determined that the target monitoring muck vehicle identification has an abnormal use behavior, and an alarm message may be sent to a set monitoring terminal device to notify related personnel to handle the abnormal use behavior.

Therefore, based on the above content, in this embodiment, the muck vehicle moving route of the target muck vehicle and the vehicle license moving route of the muck vehicle license corresponding to the target muck vehicle in the preset time period are obtained, so as to perform matching analysis on the muck vehicle moving route and the vehicle license moving route, and determine whether the abnormal vehicle license moving route exists in the vehicle license moving route. When the abnormal vehicle license moving route is determined to exist in the vehicle license moving route, determining the muck vehicle license corresponding to the abnormal vehicle license moving route as a target monitoring muck vehicle license, acquiring the significance characteristic identification of the current muck vehicle where the target monitoring muck vehicle license is located when the target monitoring muck vehicle license is monitored subsequently, and inquiring a vehicle positioning module corresponding to the target muck vehicle matched with the significance characteristic identification from a database according to the significance characteristic identification. And finally, acquiring first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and judging whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information. And when the target monitoring muck vehicle certificate is inconsistent with the target muck vehicle in positioning, determining that the target monitoring muck vehicle certificate has abnormal use behaviors, and sending alarm information to set monitoring terminal equipment so as to inform related personnel to process the abnormal use behaviors. Therefore, the abnormal condition of the muck car license can be realized by combining the moving routes of the muck car license and the muck car, and the muck car corresponding to the abnormal condition is subjected to networking positioning monitoring.

In this embodiment, as shown in fig. 4, in an alternative implementation, as for step S200, the performing matching analysis on the movement route of the muck car and the movement route of the car license to determine whether there is an abnormal movement route of the car license in the movement route of the car license may include the following steps S2001-S2006, which are exemplarily described as follows.

Step S2001, matching the movement route of the muck vehicle and the movement route of the car license in the preset time period based on the movement route matching relationship between the movement route of the muck vehicle and the movement route of the car license in the preset time period to obtain a plurality of movement route combinations.

In this embodiment, the moving route combination may include at least one set of a moving route of the muck vehicle and a moving route of the license, which are matched with each other. The matching of the movement route of the muck vehicle and the movement route of the car license can mean that the matching degree of the routes (such as the number of the included positioning position matching) between the two reaches a preset matching degree. Meanwhile, the matching of the movement route of the muck vehicle and the movement route of the license means that the muck vehicle license and the target muck vehicle are integrated in the corresponding movement route and accord with the use specification, otherwise, the matching means that the muck vehicle license and the target muck vehicle may not accord with the use specification.

For example, in one possible implementation, the license moving route in the preset time period may be determined as the license moving route to be matched, and the muck vehicle moving route in the preset time period may be determined as the muck vehicle moving route to be matched; the vehicle license position information in the vehicle license moving route to be matched is acquired through the target vehicle license identification equipment within the preset time period;

then, acquiring the position information of the muck vehicle in the target vehicle license identification equipment, and determining the position information correlation between the position information of the muck vehicle in the target vehicle license identification equipment and the position information of the muck vehicle in the movement route of the muck vehicle to be matched as the movement route matching relation between the movement route of the muck vehicle to be matched and the movement route of the muck vehicle to be matched;

and finally, when the matching relation of the moving routes reaches a set condition, matching the moving route of the license to be matched with the moving route of the muck vehicle to be matched to obtain a plurality of moving route combinations.

Step S2002, determining the matched remaining vehicle license moving route as a vehicle license moving route to be processed, and acquiring first moving route description information of the vehicle license moving route to be processed according to first vehicle license position information included in the vehicle license moving route to be processed.

In this embodiment, the first license location information is generated in the preset time period; and the vehicle license moving route in each moving route combination respectively comprises second vehicle license position information in the preset time period. The matched residual vehicle license moving routes represent vehicle license moving routes which are not matched with corresponding residual soil vehicle moving routes, and abnormal conditions that the residual soil vehicle licenses are used illegally may exist, so that the vehicle license moving routes are listed as vehicle license moving routes to be processed for further analysis and use. The first moving route description information may be a position sequence or a position matrix composed of each position point in the corresponding muck vehicle moving route, and a specific expression form is not limited in this embodiment.

Further, in a possible embodiment, the number of the first license location information may include a plurality. On this basis, in step S2002, the following may be included to obtain the first moving route description information of the to-be-processed vehicle license moving route according to the first vehicle license position information included in the to-be-processed vehicle license moving route.

And (I) acquiring position description information corresponding to each piece of first license position information in the plurality of pieces of first license position information. The location description information may at least include location coordinates and vehicle license identification information corresponding to the first vehicle license location information, and the like, which are not limited specifically.

And (II) acquiring a first position information sequence corresponding to the plurality of first vehicle license position information according to the position description information corresponding to each first vehicle license position information. The first position information sequence may be obtained by sequentially adding a plurality of position description information to a set sequence or queue.

(III) determining the first position information sequence as the first moving route description information.

Step S2003, respectively obtaining second movement route description information of the car license movement route in each movement route combination according to the second vehicle license position information included in each movement route combination.

In this embodiment, the second moving route description information is similar to the first moving route description information, and may be a position sequence or a position matrix composed of each position point in the corresponding vehicle license moving route, and a specific expression form of this embodiment is not limited.

Similarly to the above-mentioned obtaining manner of the first moving route description information, the obtaining of the second moving route description information of the vehicle license moving route in each moving route combination according to the second vehicle license position information included in each moving route combination may include the following:

firstly, acquiring position description information corresponding to each piece of second vehicle license position information in a plurality of pieces of second vehicle license position information included in each moving route combination;

then, according to the position description information corresponding to each second vehicle license position information, obtaining a second position information sequence corresponding to the plurality of second vehicle license position information;

and finally, determining the second position information sequence as second moving route description information of the vehicle license moving route in the moving route combination.

In step S2004, a difference in characteristics between the first movement route description information and the second movement route description information corresponding to the vehicle license movement route in each of the movement route combinations, respectively, is acquired.

In this embodiment, the feature difference may be used to express similarity or correlation between the first moving route description information corresponding to the to-be-processed vehicle license moving route and the second moving route description information corresponding to the vehicle license moving route in the moving route combination, and may be represented by a feature distance between feature vectors. The smaller the difference in characteristics, the higher the similarity or correlation, and thus the less likely the anomaly indicating the movement of the road evidence to be processed is (e.g., the road information may be generated by the driver briefly taking the road evidence from the road evidence). Conversely, the greater the difference in the characteristics, the lower the similarity or relevance, thereby indicating a greater likelihood of an abnormality in the movement route of the driver license to be processed (e.g., may be borrowed in violation or used by a fake-licensed vehicle).

Step S2005, determining a route association degree between the vehicle license moving route in each moving route combination and the vehicle license moving route to be processed, respectively, according to the feature difference corresponding to each moving route combination.

For example, the feature difference may be an area distance between areas defined by position points in each moving route, and the greater the area distance, the greater the feature difference, and further the smaller the route association degree between the two moving routes is identified, the higher the abnormality degree of the corresponding to-be-processed vehicle license moving route is.

Step 2006, when the number of abnormal evidence movement routes with the corresponding route relevance smaller than the preset relevance reaches the set route number, taking the muck evidence corresponding to the abnormal evidence movement route as a target monitoring muck evidence.

In this embodiment, the abnormal vehicle license moving route is included in the to-be-processed vehicle license moving route. The set number of routes may be set according to actual conditions, for example, the set number of routes may be set to 1 on the premise that the abnormal movement route of the license is determined to be abnormal in use of the muck license. In other cases, the number of the holes may be 2, 3, 4, 5, etc., and is not particularly limited.

On the basis of the above, for step S100, the obtaining of the moving route of the muck car of the target muck car and the car license moving route of the muck car license corresponding to the target muck car within the preset time period may include the following steps, which are exemplarily described as follows.

(1) And acquiring the position information of the plurality of the muck trucks and the position information of the plurality of the certificates in the preset time period.

(2) And acquiring the muck vehicle position information association degree and the muck vehicle position distance among the plurality of muck vehicle position information, and acquiring the vehicle license position information association degree and the vehicle license position distance among the plurality of vehicle license position information.

(3) Combining the position information of the plurality of muck vehicles according to the relevance of the position information of the muck vehicles and the position distance of the muck vehicles to obtain a moving route of the muck vehicles in the preset time period; wherein, a path of the slag car comprises at least two pieces of slag car position information.

(4) And combining the plurality of pieces of vehicle license position information according to the vehicle license position information association degree and the vehicle license position distance to obtain the vehicle license moving route in the preset time period. Wherein, a vehicle license moving route comprises at least two pieces of vehicle license position information.

On the basis of the above content, the embodiment of the present invention may further perform a process of identifying whether the current muck truck has a fake plate behavior when it is determined that the target monitored muck truck certificate is inconsistent with the target muck truck in positioning, which is described in the following exemplary description.

Firstly, when the target monitoring muck vehicle license is judged to be inconsistent with the target muck vehicle according to the first positioning information and the second positioning information, a monitoring video of the current muck vehicle where the target monitoring muck vehicle license is located can be obtained. The monitoring video of the current muck truck can be obtained by performing video monitoring shooting on the current muck truck according to monitoring equipment of the muck truck on the current site when the target monitoring muck truck certificate is monitored.

Then, carrying out fake plate identification on the current muck truck according to the monitoring video, and judging whether fake plate behaviors exist in the current muck truck or not;

and finally, when judging that the current muck truck has the fake-licensed behavior, sending alarm information to set monitoring terminal equipment to inform related personnel to process the fake-licensed behavior.

In this embodiment, the fake-plate identification of the current muck truck according to the monitoring video and the judgment of whether the current muck truck has fake-plate behavior can be realized through the following steps 1 to 5.

1. And acquiring a monitoring image frame from the monitoring video as a current monitoring image frame and acquiring at least one monitoring image frame behind the current monitoring image frame as a fusion monitoring image frame in sequence.

For example, as an alternative example, the surveillance video may include individual surveillance image frames represented by: frame _1, Frame _2, Frame _3, a. Then, the surveillance video may include a plurality of surveillance image frames of Frame _1 to Frame _ m. Then, when the image Frame is acquired for the first time, the Frame _1 may be used as the current monitoring image Frame, and the Frame _2 may be used as the fused monitoring image Frame. When the image Frame is acquired for the second time, the Frame _2 can be used as the current monitoring image Frame, and the Frame _3 can be used as the fusion monitoring image Frame; when the image Frame is acquired at the nth time, Frame _ n may be used as the current monitoring image Frame, and Frame _ n +1 may be used as the fused monitoring image Frame, and the above steps are repeated.

For another example, the number of the fused monitored image frames may be two or more, for example, taking two fused monitored image frames each time as an example, when image Frame acquisition is performed for the first time, Frame _1 may be used as the current monitored image Frame, and Frame _2 and Frame _3 may be used as the fused monitored image Frame; when image Frame acquisition is performed for the second time, the Frame _2 may be used as the current monitoring image Frame, and the Frame _3 and the Frame _4 may be used as the fused monitoring image Frame; when the image Frame is acquired at the nth time, Frame _ n may be used as the current monitoring image Frame, and Frame _ n +1 and Frame _ n +2 may be used as the fused monitoring image Frame, and the above steps are repeated.

Therefore, key information collection is carried out on the current monitoring image frame according to the significance identification information, the collected identification feature vector with the significance identification information in the first key visual feature is obtained, then key information collection is carried out on the fusion monitoring image frame according to the significance identification information, and the obtained identification feature vector with the significance identification information in the second key visual feature is also obtained.

2. And respectively acquiring key information of the current monitoring image frame and the fused monitoring image frame according to the significance identification information of the current muck truck to obtain a first key visual feature of the current monitoring image frame and a second key visual feature of the fused monitoring image frame.

In this embodiment, the first key visualization feature is at least used to characterize a feature association degree between the visualization feature included in the current monitored image frame and the saliency identification information, and the second key visualization feature is at least used to characterize a feature association degree between the visualization feature included in the fused monitored image frame and the saliency identification information.

The significant identification information may at least include any one or a combination of two or more of license plate number characteristics, muck vehicle brand characteristics and vehicle body visualization characteristics (such as color, length, width and height), so that monitoring image frames which may include the basic characteristics are firstly identified through the characteristics, corresponding characteristics are obtained through the corresponding monitoring image frames, and the monitoring image frames are combined and then sent to the cloud platform for precise identification. For example, after feature combination is performed on two or more different image frames, feature combination of the muck vehicle under different vision (such as different vehicle postures) can be obtained, and accuracy of subsequent identification of the muck vehicle fake plate can be improved.

The acquiring, according to the saliency identification information, key information of the current monitored image frame to obtain a first key visualization feature of the current monitored image frame may include:

firstly, collecting key information of the significance identification information to obtain an identification feature vector of the significance identification information;

secondly, performing key information acquisition on two or more interested areas in the current monitoring image frame to obtain key information of the interested areas of the two or more interested areas;

then, determining the region association levels of the two or more regions of interest based on the identification feature vector and the region of interest key information of the two or more regions of interest, wherein the region association levels of the regions of interest characterize the feature association degree between the regions of interest and the significance identification information;

and finally, performing information fusion on the key information of the interest areas of the two or more interest areas based on the area association levels of the two or more interest areas to obtain the first key visualization feature.

3. And combining the first key visualization feature and the second key visualization feature to obtain a key visualization combination feature of the current monitoring image frame.

In detail, in order to enable the first key visualization feature to have more related feature contents capable of describing the visualization feature, the first key visualization feature and the second key visualization feature may be combined to obtain a key visualization combination feature of the current monitored image frame, where the key visualization combination feature includes not only the visualization feature included in the current monitored image frame but also the visualization feature included in the fused monitored image frame.

Therefore, the second key visual feature of the fused monitoring image frame behind the current monitoring image frame is fused into the first key visual feature of the current monitoring image frame, so that the obtained key visual combination feature of the current monitoring image frame not only contains the visual feature included by the current monitoring image frame, but also contains the visual feature included by the fused monitoring image frame behind the current monitoring image frame, the key visual combination feature can more comprehensively express the visual feature of the current monitoring video, and further the related image feature of the currently monitored muck vehicle is more accurately expressed according to the key visual combination feature, so that the accuracy of the subsequent muck vehicle fake plate identification result is better. In addition, as the key visualization features are acquired according to the saliency identification information, the key visualization features can also reflect the feature association degree of the visualization features included in the aerial photography monitoring image frame and the visualization features represented by the saliency identification information.

In one possible implementation, when the fused monitoring image frame may include two or more. Based on this, the combining the first key visualization feature and the second key visualization feature to obtain the key visualization combination feature of the current monitored image frame may include:

4. And analyzing the matching degree of the key visual combined features and the standard visual features which are stored in the database and correspond to the significance identification information, and judging that the current muck truck has no fake plate behavior when the standard visual features which are stored in the database and correspond to the significance identification information are matched with the key visual combined features.

5. And when the standard visual characteristics corresponding to the significance identification information and the key visual combination characteristics stored in the database are not matched, judging that the current muck truck has a fake plate behavior.

As shown in fig. 5, is a schematic diagram of functional modules included in the fake-licensed muck car networking positioning system 24 in fig. 2. In some other possible embodiments, the fake-licensed muck internet-of-vehicle positioning system 24 may be the cloud platform 20 itself, the processor 22 of the cloud platform 20, or an external component independent from the cloud platform 20 and the processor 22, which is not limited in this embodiment.

Preferably, in the present embodiment, the fake-licensed muck internet of vehicles positioning system 24 may include a first obtaining module 241, a matching analysis module 242, an abnormality determination module 243, a second obtaining module 244, a positioning analysis module 245, and an abnormality alarm module 246.

The first obtaining module 241 is configured to obtain a muck vehicle moving route of a target muck vehicle and a vehicle license moving route of a muck vehicle license corresponding to the target muck vehicle within a preset time period.

And the matching analysis module 242 is configured to perform matching analysis on the muck vehicle moving route and the vehicle license moving route, and determine whether an abnormal vehicle license moving route exists in the vehicle license moving route.

And an anomaly determining module 243, configured to determine, when it is determined that an abnormal vehicle license moving route exists in the vehicle license moving routes, a muck vehicle license corresponding to the abnormal vehicle license moving route as a target monitoring muck vehicle license.

The second obtaining module 244 is configured to, when the target monitoring muck vehicle identification is monitored, obtain a significant feature identifier of a current muck vehicle where the target monitoring muck vehicle identification is located, and query, according to the significant feature identifier, a vehicle positioning module corresponding to a target muck vehicle matched with the significant feature identifier from a database.

And the positioning analysis module 245 is configured to acquire first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and determine whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information.

And an abnormal alarm module 246, configured to determine that there is an abnormal usage behavior of the target monitoring muck vehicle certificate when it is determined that the target monitoring muck vehicle certificate is inconsistent with the target muck vehicle positioning according to the first positioning information and the second positioning information, and send alarm information to a set monitoring terminal device to notify relevant personnel to handle the abnormal usage behavior.

The matching analysis module 242 is specifically configured to:

In addition, in this embodiment, the fake-licensed muck internet of vehicles positioning system may further include a fake-licensed identification module 247 configured to:

Wherein, carry out fake plate discernment to current dregs car according to the surveillance video, judge whether current dregs car has fake plate action, include:

performing matching degree analysis on the key visual combined characteristic and a standard visual characteristic which is stored in the database and corresponds to the significance identification information, and judging that the current muck truck has no fake plate behavior when the standard visual characteristic which is stored in the database and corresponds to the significance identification information is matched with the key visual combined characteristic;

It should be understood that the functional modules described above may respectively correspond to corresponding steps in the foregoing method embodiments, and details of the corresponding steps may be referred to for details of the modules, which are not described herein again.

In summary, according to the fake-licensed muck car networking positioning method, system and cloud platform provided by the embodiments of the present invention, a muck car moving route of a target muck car and a car license moving route of a muck car license corresponding to the target muck car in a preset time period are obtained, so as to perform matching analysis on the muck car moving route and the car license moving route, and determine whether an abnormal car license moving route exists in the car license moving route. When the abnormal vehicle license moving route is determined to exist in the vehicle license moving route, determining the muck vehicle license corresponding to the abnormal vehicle license moving route as a target monitoring muck vehicle license, acquiring the significance characteristic identification of the current muck vehicle where the target monitoring muck vehicle license is located when the target monitoring muck vehicle license is monitored subsequently, and inquiring a vehicle positioning module corresponding to the target muck vehicle matched with the significance characteristic identification from a database according to the significance characteristic identification. And finally, acquiring first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and judging whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information. And when the target monitoring muck vehicle certificate is inconsistent with the target muck vehicle in positioning, determining that the target monitoring muck vehicle certificate has abnormal use behaviors, and sending alarm information to set monitoring terminal equipment so as to inform related personnel to process the abnormal use behaviors. Therefore, the abnormal condition of the muck car license can be realized by combining the moving routes of the muck car license and the muck car, and the muck car corresponding to the abnormal condition is subjected to networking positioning monitoring.

The embodiments described above are only a part of the embodiments of the present invention, and not all of them. The components of embodiments of the present invention generally described and illustrated in the figures can be arranged and designed in a wide variety of different configurations. Therefore, the detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the present invention, but is merely representative of selected embodiments of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims. Moreover, all other embodiments that can be made available by a person skilled in the art without inventive step based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

Claims

1. The fake plate slag car networking positioning method is applied to a cloud platform, and comprises the following steps:

acquiring first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and judging whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information;

the cloud platform is a service platform which is preset and is used for being in communication connection with vehicle certificate identification equipment and vehicle monitoring equipment for performing related monitoring and identification on the muck vehicle, and the vehicle certificate identification equipment is FRID reading equipment for reading and identifying information of RFID vehicle card certificates;

the vehicle license moving route is obtained through historical monitoring data fed back by identifying the muck vehicle license through the vehicle license identification device, or is obtained through historical positioning information recorded by the muck vehicle license when the muck vehicle license has an information storage function.

2. The method of claim 1, further comprising:

3. The method of claim 1, wherein the matching analysis of the muck vehicle movement route and the vehicle evidence movement route to determine whether an abnormal vehicle evidence movement route exists in the vehicle evidence movement route comprises:

4. The method according to claim 3, wherein the first license location information is plural in number; the obtaining of the first moving route description information of the vehicle license moving route to be processed according to the first vehicle license position information contained in the vehicle license moving route to be processed comprises:

the step of respectively acquiring second movement route description information of the vehicle license movement route in each movement route combination according to the second vehicle license position information included in each movement route combination comprises the following steps:

5. The method according to claim 3, wherein the obtaining of the movement route of the muck vehicle of the target muck vehicle and the movement route of the car license of the muck vehicle corresponding to the target muck vehicle within the preset time period comprises:

6. The method of claim 1, further comprising:

7. The method of claim 6, wherein said identifying the current slag car as a fake plate according to the surveillance video and determining whether the current slag car has fake plate behavior comprises:

performing matching degree analysis on the key visual combined feature and a standard visual feature which is stored in a database and corresponds to the significance identification information, and judging that no fake plate behavior exists in the current muck truck when the standard visual feature which is stored in the database and corresponds to the significance identification information is matched with the key visual combined feature;

8. The method according to claim 7, wherein performing key information acquisition on the current monitored image frame according to the saliency identification information to obtain a first key visualization feature of the current monitored image frame comprises:

9. A fake plate muck car networking positioning system is characterized in that the fake plate muck car networking positioning system is applied to a cloud platform and comprises

the positioning analysis module is used for acquiring first positioning information of the target muck vehicle and second positioning information of the target monitoring muck vehicle certificate through the vehicle positioning module, and judging whether the target monitoring muck vehicle certificate and the target muck vehicle are positioned consistently according to the first positioning information and the second positioning information;

10. A cloud platform comprising a processor, a machine-readable storage medium coupled to the processor, the machine-readable storage medium configured to store a program, instructions, or code, and the processor configured to execute the program, instructions, or code in the machine-readable storage medium to implement the method of any one of claims 1-8.